A user is commonly exposed to multiple marketing channels. The purchase journey takes many roads: e-mail, mobile, display advertising, social media and so on. All these impressions have an influence on the final decision of the user. To maximize conversions (e.g., purchases of the user), a marketer needs to understand how each of these marketing efforts affects the final decision and, accordingly, optimize the advertising budget over the marketing channels. Interpreting the influence of various marketing channels to the user's decision process is called marketing attribution.
The marketer uses different marketing channels across different users. The different marketing channels form different journeys of exposure. For example, while one user is exposed to e-mail and mobile advertisements, another user is also exposed to social media advertisement. The conversion of each user can be more influenced by a particular marketing channel (e.g., e-mail advertisement). However, more often than not, the total journey has a higher influence on the user's conversion. Thus, a proper marketing attribution analysis should consider the different journeys of the users.
Traditionally, marketing attribution uses models that assign the influence to each marketing channel in a rule-based manner, which is often non-intuitive. For example, whereas the user is exposed to a combination of marketing channels, a rule allocates the highest attribution to the last marketing channel that the user is exposed to before a purchase decision. However, this type of attribution allocation can be inaccurate. For example, the rules can fail to properly capture the influence of the other intermediary marketing channels on the user's purchase decision.
Other approaches have also been adopted. These approaches generally use algorithmic models, such as ones that implement regression functions. Typically, the algorithmic models make assumptions around particular parameters to estimate the marketing attributions. The accuracy of the estimation depends on the assumptions.
Commonly, relationships between exposures to “k” marketing channels and user conversion are assumed. For example, an exposure to “k” marketing channel is assumed to result in a conversion (e.g., purchase) at a certain likelihood. A logistic regression function is used to estimate the attribution of each marketing channel. The accuracy of estimation largely depends on how well the assumed relationships map to the actual relationships. Generally, the actual relationships are unknown, potentially non-linear (e.g., a higher number of marketing channels does not translate into a higher conversion likelihood) and may show synergistic effects (exposure to one marketing channel affects the influence of another marketing channel on the conversion). Thus, by relying on assumed relationships, analyzing the marketing attributions can involve some inaccurate estimations.